Fabrication of Copper Oxide Modified Graphene Composite Sensor for Simultaneous Detection of Biometabolites from Urine
Bibliographic record
Abstract
It is a common knowledge that the concentration of certain biomarkers in the body accounts for the presence of some diseases. For instance, the unusual levels of uric acid in body fluids may be indicative of gout and hyperuricemia. These biomarkers may be present within the body but may not be detected at very low concentrations and on time to enable proactive treatments, and the consequences may be dire. The current research report features a new approach developed towards fabricating a new copper oxide modified graphene composite sensor with unique nanostructures on a pencil electrode for sensing three biometabolites (xanthine, uric acid and urea) in urine at very low limit of detection (LOD). The surface structures of this composite sensor composed of several electroactive sites for adsorption of desired analytes. This sensor was appropriately characterized by means of Raman spectroscopy, scanning electron microscopy, cyclic voltammetry as well as localized and normal electrochemical impedance spectroscopy. This sensor was significantly sensitive towards uric acid ((LOD = 10 ppm) and urea (LOD = 20 ppm) sensing compared to xanthine (LOD = 50 ppm). Optimization of key sensor parameters towards efficient sensing (e.g. CuO content) were also examined. The efficiency of the composite sensor toward sensing these analytes in the presence of potential interference was investigated using real human urine. Biomolecular sensing of disease biomarkers at low LOD is important in the assessment of health conditions and monitoring treatment progress of patients. Our experimental results demonstrate the efficacy of simultaneously sensing multiple analytes in a complex biological sample. Keywords: Biosensing; Simultaneous detection; Biometabolites; Disease biomarker; Limit of detection
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".